Prediction and compensation of subsequent deformation in robotbased incremental sheet forming by application of machine learning

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Incremental sheet forming (ISF) is flexible, workpiece-independent process for manufacturing sheet metal parts in small lot sizes. An industrial application has yet rare or not all taken place due to the still low geometric accuracy of the process. The reason for this is mainly the missing possibility for a precise simulation of the forming process, hindering the use of compensation approaches for springback and subsequent deformation. While there are multiple FEM-based simulation approaches, their application is prevented by summing up simulation errors, caused by the incremental nature of the process. During the research project, a data driven approach will be pursued by the usage of machine learning, which, in contrast to FEM-simulations, does not need a detailed modelling of the forming process. A multi-layer artificial neural network (ANN) will be build up, predicting the resulting geometric accuracy of a forming experiment based on common process parameters, part geometry and the course of the tool path.To be able to train the ANN, a process database will be built up. A preferably wide spectrum of process data will be acquired in an experimental series, in which a systematically varied part will be formed and measured with alternated process parameters. By the this way generated wide range of the training data, a generalisation of the ANN is enabled making it applicable to any part.To take the influence of the part geometry on the resulting geometric accuracy into account, the part geometry will be transformed into a format with a fixed number of parameters which is therefore usable for machine learning. This is achieved by the development of several representation approaches whose performances are evaluated with a quality criterion. This assesses the quality of the approximation and correlation of the parameters of the geometry representation and the part geometry.Utilizing the built up process database and the developed geometry representation with the highest performance, a multi-layer ANN will be trained. Meanwhile its prediction performance is validated with a test dataset gathered in reference forming experiments. Afterwards the trained ANN is used to improve the geometric accuracy of a part by modifying the tool path based on the predicted geometric accuracy. To execute this, existing tool path planning approaches need to be extended as the data driven nature of the ANN can lead to rare prediction errors otherwise resulting in false tool paths.